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https://wiki.med.harvard.edu/SysBio/Megason/GoFigure

GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy   

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http://connectomics.org/viewer

Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.

Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy   

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http://www.archer.edu.au/

The ARCHER project is built upon the prototype software developed by the DART (http://dart.edu.au) and ARROW (http://arrow.edu.au) projects to produce a robust set of software tools. These tools: - may be customised to suit the needs of diverse research areas - automate the collection and management of instrument generated data - enable the repository storage of research data and associated metadata - enable collection and tagging of research data in a collaborative environment, and - provide these capabilities in a secure end-to-end proces. :ARCHER developed a ''production-ready'' software tools, operating in a secure environment, to assist researchers to: - collect, capture and retain large data sets from a range of different sources including scientific instruments - deposit data files and data sets to eResearch storage repositories - populate these eResearch data repositories with associated metadata - permit data set annotation and discussion in a collaborative environment, and - support next-generation methods for research publication, dissemination and access.

Proper citation: Australian ResearCH Enabling enviRonment (RRID:SCR_008390) Copy   

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http://tuna.tamu.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Map improvement server that returns a bias minimized, 6-fold averaged map generated from a model and diffraction data (with optional preceding Molecular Replacement). It does not build or repair the model for you (yet). For automated model building, you need to install a local copy of CCP4 and ARP/wARP (aka wARP&Trace), RESOLVE, MAID, or TEXTAL.

Proper citation: TB Consortium Bias Removal Server (RRID:SCR_008425) Copy   

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http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

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http://www.biobankcentral.org/resource/wwibb.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 27, 2013. Web-based portal to connect all the constituencies in the global biobank community. The project seeks to increase the transparency and accessibility of the scientific research process by connecting researchers with an additional source of funding - microinvestments received from the broader online community. In exchange for these public investments, researchers will maintain research logs detailing the play-by-play progress made in their project, as well as publishing all of their data in a public database under a science commons license. These research projects, in turn, will serve to continually update a research-based neuroscience-based human brain & body curriculum. Biobanks are the meeting point of two major information trends in biomedical research: the generation of huge amounts of genomic and other laboratory data, and the electronic capture and integration of patient clinical records. They are comprised of large numbers of human biospecimens supplemented with clinical data. Biobanks when implemented effectively can harness the power of both genomic and clinical data and serve as a critical bridge between basic and applied research, linking laboratory to patient and getting to cures faster. As science and technology leaders work to address the many challenges facing U.S. biobanks logistical, technical, ethical, financial, intellectual property, and IT BioBank Central will serve as an accurate and timely source of knowledge and news about biorepositories and their role in research and drug development. The Web site also provides a working group venue, patient and public education programs, and a forum for international collaboration and harmonization of best practices.

Proper citation: BioBank Central (RRID:SCR_008645) Copy   

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http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=nuro

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Portal that provides researchers with easy access to data on rat genes, QTLs, strain models, biological processes and pathways related to neurological diseases. This resource also includes dynamic data analysis tools.

Proper citation: Rat Genome Database: Neurological Disease Portal (RRID:SCR_008685) Copy   

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http://www.blogmydata.org/

Software tool for data sharing, incorporating blogs and spatial registration of data. Mainly used in geological data sets. A Virtual Research Environment (VRE) aims to combine the capabilities of two existing technologies that have already seen wide adoption among scientists: - The Godiva2 data visualization system provides a means for scientists to browse interactively in a ''Google Maps-like'' fashion through large environmental datasets, including numerical model outputs and high-resolution satellite imagery, using only a web browser. - The LabBlog is a web-based blogging tool specifically designed for the practising scientist to record, disseminate and evaluate their research. The Blog can also be used as a collaboration tool that allows secure discussion between colleagues. Although initially designed for the use of laboratory chemists, the LabBlog is being adapted in this project to meet the needs of environmental scientists. The BlogMyData VRE will allow scientists to explore data visually using Godiva2, then make comments about features in the data on a blog. Colleagues will discover these blog entries and offer further information, providing answers to research questions through comments. Through RSS and GeoRSS feeds, colleagues, investigators and other interested parties can be notified of research activity, and scientists can discover hitherto-unknown colleagues working with similar data in similar geographic regions. Sponsors: BlogMyData is a collaboration between the Reading e-Science Centre and the University of Southampton and is one of the JISC VRERI projects.

Proper citation: BlogMyData (RRID:SCR_008697) Copy   

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http://openii.sourceforge.net/

OpenII (pronounced open-eye-eye) is a freely downloadable, open source information integration (II) tool suite. It includes 1) an extensible, plug-and-play platform for II tools and 2) several tools that assist with common integration tasks, including fully- or semi-automated support in the following scenarios: :- An integration engineer building a data warehouse must determine how diverse component data schemas map to the schema of the warehouse. :- An XML document that conforms to one schema needs to be converted into an equivalent document that conforms to a second (different) schema. :- To support data exchanges, a community needs to create a shared data model based on the models of its members. When a new member joins, the community needs to identify promising data exchange partners, and to what extent its shared model needs to be extended. Similarly, a chief information officer must identify data integration opportunities and make level-of-effort estimates after an acquisition or merger. To support these scenarios, OpenII provides a schema repository into which diverse data models can easily be imported. It also provides tools that 1) assist with identifying semantic correspondences across data models (Harmony), 2) compare a set of data models against a common reference model (Proximity), 3) visually organize a set of data models into clusters of related data models (Affinity), and 4) establish a common data model for a set of inter-related data models (Unity). Why should You use OpenII? Here are some reasons: :- OpenII is the only open-source platform for information integration tools. OpenII and its source code are freely available using the Apache 2.0 license, so you are free to borrow, extend or resell any portions of OpenII. :- The OpenII schema and mapping repository is based on a neutral modeling language. Thus, all of the OpenII tools can be used regardless of the underlying modeling language. For example, Harmony can identify correspondences among an XML schema, a relational database, and an OWL ontology. By comparison, most commercial tools are tied to a particular modeling language. :- OpenII is based on the Eclipse framework. As a result, the environment is already familiar to many programmers. Non-programmers can choose, instead, to use OpenII off-the-shelf without needing to first install Eclipse. :- OpenII is fully extensible. If needed components do not exist, they can be readily added. For example, adding a new importer or exporter is a straightforward task that can be completed in only a few hours. Moreover, each of the tools supports the introduction of new algorithms. And, programmers familiar with the Eclipse environment can add new views with moderate effort. Sponsors: This resource is supported by the MITRE Corporation.

Proper citation: Open Information Integration (RRID:SCR_008699) Copy   

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http://neuro-information.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. The International Observatory on Neuro-Information is the central source of knowledge, research and data on all skills and issues for Neuroscience applied in Information Sciences. It is an initiative of the Documentation Sciences Foundation, from Spain, which aims to gather information, express opinions, prepare documents, make comparative research, support and promote policy-making, evaluate trends, and take other appropriate action relating to the Neuroscience and its application to the Information Sciences (Libraries, Archives, Documentation centers), and how the traditional knowledge of Information Sciences can bring expertise in data visualization and retrieval techniques, records management, quality assurance and usability in Neuroscience. The Observatory may work together, or in agreement with other national or international organizations pursuing similar or compatible aims.

Proper citation: International Observatory on Neuro-Information (RRID:SCR_008690) Copy   

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https://www.brainproducts.com/

Commercial organization for hardware and software for neurophysiological research. Provides EEG and ERP amplifier systems, EEG recording caps, Data recording and analysis software, TMS Stimulator for combined EEG/TMS coregistrations and more.

Proper citation: Brain Products (RRID:SCR_009443) Copy   

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http://wbiomed.curtin.edu.au/genepop/

Population genetic data analysis software package. Used to perform exact Hardy Weinberg Equilibrium test. Used for population differentiation and for genotypic disequilibrium among pairs of loci. Computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc. and performs analyses of isolation by distance from pairwise comparisons of individuals or population samples., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GENEPOP (RRID:SCR_009194) Copy   

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https://www.mdc-berlin.de/8551903/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep

Software tool to identify known and novel miRNA genes in seven animal clades by analyzing sequenced RNAs. Used for discovering known and novel miRNAs from small RNA sequencing data.

Proper citation: miRDeep (RRID:SCR_010829) Copy   

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http://www.ime.fraunhofer.de/en.html

Provides assistance with consulting on experimental design, training on bioinformatics tools and databases, data quality assessment, data processing, data visualization, data interpretation, data mining of published datasets, and assistance with preparation of manuscripts and grant proposals.

Proper citation: Mainz Institute of Molecular Biology Bioinformatics Core Facility (RRID:SCR_011244) Copy   

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http://www.sanger.ac.uk/

Non profit research organization for genome sequences to advance understanding of biology of humans and pathogens in order to improve human health globally. Provides data which can be translated for diagnostics, treatments or therapies including over 100 finished genomes, which can be downloaded. Data are publicly available on limited basis, and provided more extensively upon request.

Proper citation: Wellcome Trust Sanger Institute; Hinxton; United Kingdom (RRID:SCR_011784) Copy   

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https://www.immport.org/home

Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.

Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy   

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http://web.bioinformatics.ic.ac.uk/eqtlexplorer/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 1,2023. eQTL Explorer was developed as a computational resource to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. This visualization tool stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer also provides a new and powerful tool to interrogate these very large and complex datasets. eQTLexplorer allows users to mine and understand data from a repository of genetical genomics experiments. It will graphically display eQTL information based on a certain number of selection criteria, including: tissue type, p-value, cis/trans, probeset Affymetrix id and PQTL type. Sponsors: This work was funded by the MRC Clinical Sciences Centre and the Wellcome Trust programme for Cardiovascular Functional Genomics.

Proper citation: eQTL Visualization Tool (RRID:SCR_013413) Copy   

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https://skyline.gs.washington.edu/labkey/project/home/software/Skyline/begin.view

Software tool as Windows client application for targeted proteomics method creation and quantitative data analysis. Open source document editor for creating and analyzing targeted proteomics experiments. Used for large scale quantitative mass spectrometry studies in life sciences.

Proper citation: Skyline (RRID:SCR_014080) Copy   

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http://www.originlab.com/index.aspx?go=PRODUCTS/Origin

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 4, 2025.Software application for data analysis and graphing. Origin contains a variety of different graph types, including statistical plots, 2D and 3D vector graphs, and counter graphs. More advance version is OriginPro which offers advanced analysis tools and Apps for Peak Fitting, Surface Fitting, Statistics and Signal Processing.

Proper citation: Origin (RRID:SCR_014212) Copy   

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https://t1dexchange.org/pages/

Provides access to resources T1D researchers need to conduct clinical studies. Data sets from their clinic registry is openly available, as are new study results. They also offer use of T1D Discovery Tool, which allows users to search different fields from registry data, and T1D Exchange Biobank, which offers specimen types such as serum, plasma, white blood cells, DNA, and RNA.

Proper citation: T1D Exchange (RRID:SCR_014532) Copy   

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